Abstract

Patient-derived xenograft (PDX) is a well-accepted experimental cancer model mimicking original patients in histo- & molecular pathology, as well as drug response. Mouse clinical trials (MCT) using PDX becomes increasingly prevalent in oncology research, yet the theory and methodology for trial design and analysis is underdeveloped. By analyzing tumor growths of 34081 PDX mice, we showed that majority of them can be satisfactorily modeled by exponential growth kinetics, based on which we derived a new drug response readout called the AUC ratio that is superior to the tumor growth inhibition (TGI) and its variants. We outlined a bootstrap algorithm to calculate its confidence interval, and showed that a 4:4 (4 mice in both treatment groups) or up design sharply increases measurement accuracy over the lesser design. Next, we introduce three approaches for drug evaluation and biomarker discovery in MCTs. In the end-point based analysis, we used a cohort of gastric PDXs to show a positive correlation between EGFR mRNA expression and cetuximab efficacy, in agreement with clinical trial results. We then used linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies that capture the growth and drug response heterogeneities across PDXs and among mice within a PDX. Further, LMMs can separate prognostic and predictive biomarker effects, quantify response difference to a drug for multiple cancers, or efficacy difference of multiple drugs to a cohort of PDXs. Thirdly, we used additive frailty models to perform survival analysis on MCTs. We defined survival endpoints of PFS and OS in PDXs, and showed that hazard ratios can be more accurately estimated. We revealed an inherent connection between frailty and growth rate for PDXs. We performed computational simulations for the last two methods to show that statistical power improves tremendously with a 3:3 or up design. This work lays the foundation for rational design and analysis of PDX mouse clinical trials.